Daemi Amin, Kalami Sahar, Tahiraga Ruhiyya Guliyeva, Ghanbarpour Omid, Barghani Mohammad Reza Rahimi, Hooshiar Mohammad Hosseini, Özbolat Gülüzar, Yönden Zafer
Department of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana, Turkey.
Department of Biology, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
Clin Exp Med. 2025 Jul 18;25(1):255. doi: 10.1007/s10238-025-01723-x.
Artificial intelligence (AI) is transforming the landscape of laboratory medicine by enhancing diagnostic accuracy and enabling more personalized care. Given its growing use in clinical settings, evaluating the performance of AI models in diagnostic tasks is essential to inform evidence-based implementation strategies. This meta-analysis systematically assessed the diagnostic effectiveness of AI-based models. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore using predefined keywords related to AI and diagnostic accuracy. From 430 retrieved studies, 17 met the inclusion criteria. Data extracted included study design, AI model type, input modality, and performance metrics such as sensitivity, specificity, and area under the curve (AUC). Random-effects meta-analysis and subgroup analyses were performed to investigate heterogeneity and model-specific trends. The pooled analysis yielded a high combined AUC of 0.9025, indicating strong diagnostic capability of AI models. However, substantial heterogeneity was detected (I = 91.01%), attributed to differences in model architecture, diagnostic domains, and data quality. Subgroup analyses showed that convolutional neural networks and random forest models achieved higher AUC values, while domains like endocrinology demonstrated greater performance variability. Funnel plot inspection and sensitivity analysis indicated the presence of publication bias. AI shows strong potential to enhance diagnostic accuracy in personalized laboratory medicine. Nonetheless, methodological heterogeneity and publication bias remain significant challenges. Future research should prioritize standardized evaluation frameworks, transparency, and the development of explainable AI systems to ensure responsible clinical integration.
人工智能（AI）正在通过提高诊断准确性和实现更个性化的护理来改变检验医学的格局。鉴于其在临床环境中的使用日益增加，评估人工智能模型在诊断任务中的性能对于制定基于证据的实施策略至关重要。这项荟萃分析系统地评估了基于人工智能的模型的诊断有效性。使用与人工智能和诊断准确性相关的预定义关键词，在PubMed、Scopus、Web of Science和IEEE Xplore中进行了全面的文献检索。从检索到的430项研究中，有17项符合纳入标准。提取的数据包括研究设计、人工智能模型类型、输入模式以及诸如敏感性、特异性和曲线下面积（AUC）等性能指标。进行随机效应荟萃分析和亚组分析以研究异质性和特定模型的趋势。汇总分析得出的综合AUC较高，为0.9025，表明人工智能模型具有很强的诊断能力。然而，检测到存在显著的异质性（I=91.01%），这归因于模型架构、诊断领域和数据质量的差异。亚组分析表明，卷积神经网络和随机森林模型获得了更高的AUC值，而内分泌学等领域表现出更大的性能变异性。漏斗图检验和敏感性分析表明存在发表偏倚。人工智能在个性化检验医学中提高诊断准确性方面显示出强大的潜力。尽管如此，方法学异质性和发表偏倚仍然是重大挑战。未来的研究应优先考虑标准化评估框架、透明度以及可解释人工智能系统的开发，以确保负责任的临床整合。
